This monograph presents the direct likelihood approach to statistical inference. It will be of interest to researchers and graduate students in statistics.

A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample information into various parts, such that the diverse problems of inference that arise from repeatable experiments may be addressed. A particularly valuable feature is the large number of practical examples, many of which use data taken from experiments published in various scientific journals. This book evolved from the authors own courses on statistical inference, and assumes an introductory course in probability, including the calculation and manipulation of probability functions and density functions, transformation of variables and the use of Jacobians. While this is a suitable text book for advanced undergraduate, Masters, and Ph.D. statistics students, it may also be used as a reference book.

The Likelihood Function.- Division of Sample Information I: Likelihood ?, Model f.- Division of Sample Information II: Likelihood Structure.- Estimation Statements.- Tests of Significance.- The Location-Scale Pivotal Model.- The Gauss Linear Model.- Maximum Likelihood Estimation.- Controlled Experiments.- Problems.